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Record W7073948488

Pain catastrophizing as a risk factor for chronic pain after total knee arthroplasty: a systematic review

2015· review· en· W7073948488 on OpenAlexaboutno aff

Bibliographic record

VenueDove Medical Press (Taylor and Francis Group) · 2015
Typereview
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsLearned helplessnessPsycINFOPain catastrophizingChronic painRisk factorProspective cohort studyMEDLINEInclusion and exclusion criteria
DOInot available

Abstract

fetched live from OpenAlex

Lindsay C Burns,1–3 Sarah E Ritvo,1 Meaghan K Ferguson,1 Hance Clarke,3–5 Ze’ev Seltzer,3,5 Joel Katz1,3–5 1Department of Psychology, York University, Toronto, ON, Canada; 2Arthritis Research Centre of Canada, Vancouver, BC, Canada; 3Department of Anesthesia and Pain Management, Toronto General Hospital, Toronto, ON, Canada; 4Department of Anesthesia, University of Toronto, Toronto, ON, Canada; 5Centre for the Study of Pain, Faculties of Dentistry and Medicine, University of Toronto, Toronto, ON, Canada Background: Total knee arthroplasty (TKA) is a common and costly surgical procedure. Despite high success rates, many TKA patients develop chronic pain in the months and years following surgery, constituting a public health burden. Pain catastrophizing is a construct that reflects anxious preoccupation with pain, inability to inhibit pain-related fears, amplification of the significance of pain vis-à-vis health implications, and a sense of helplessness regarding pain. Recent research suggests that it may be an important risk factor for untoward TKA outcomes. To clarify this impact, we systematically reviewed the literature to date on pain catastrophizing as a prospective predictor of chronic pain following TKA. Methods: We searched MEDLINE, EMBASE, and PsycINFO databases to identify articles related to pain catastrophizing, TKA, risk models, and chronic pain. We reviewed titles and abstracts to identify original research articles that met our specified inclusion criteria. Included articles were then rated for methodological quality. including methodological quality. Due to heterogeneity in follow-up, analyses, and outcomes reported across studies, a quantitative meta-analysis could not be performed. Results: We identified six prospective longitudinal studies with small-to-mid-sized samples that met the inclusion criteria. Despite considerable variability in reported pain outcomes, pain catastrophizing was identified as a significant predictor of chronic pain persisting ≥3 months following TKA in five of the studies assessed. Limitations of studies included lack of large-scale data, absence of standardized pain measurements, inadequate multivariate adjustment, such as failure to control for analgesic use and other relevant covariates, and failure to report non-significant parameter estimates. Conclusion: This study provides moderate-level evidence for pain catastrophizing as an independent predictor of chronic pain post-TKA. Directions for future research include larger, well-controlled studies with standard pain outcomes, identification of clinically-relevant catastrophizing cut-offs that predict pain outcomes, investigation of other psychosocial risk factors, and assessment of interventions aimed to reduce pain catastrophizing on chronic pain outcomes following TKA surgery. Keywords: pain catastrophizing, total knee arthroplasty, total knee replacement, knee arthroplasty, risk factors, chronic pain

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0060.008
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.304
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations149
Published2015
Admission routes1
Has abstractyes

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